Model-driven processes and tools to design robot-based generative learning objects for computer science education

Abstract

In this paper, we introduce a methodology to design robot-oriented generative learning objects (GLOs) that are, in fact, heterogeneous meta-programs to teach computer science (CS) topics such as programming. The methodology includes CS learning variability modelling using the feature-based approaches borrowed from the SW engineering domain. Firstly, we define the CS learning domain using the known educational framework TPACK (Technology, Pedagogy And Content Knowledge). By learning variability we mean the attributes of the framework extracted and represented as feature models with multiple values. Therefore, the CS learning variability represents the problem domain. Meta-programming is considered as a solution domain. Both are represented by feature models. The GLO design task is formulated as mapping the problem domain model on the solution domain model. Next, we present the design framework to design GLOs manually or semi-automatically. The multi-level separation of concepts, model representation and transformation forms the conceptual background. Its theoretical background includes: (a) a formal definition of feature-based models; (b) a graph-based and set-based definition of meta-programming concepts; (c) transformation rules to support the model mapping; (d) a computational Abstract State Machine model to define the processes and design tool for developing GLOs. We present the architecture and some characteristics of the tool. The tool enables to improve the GLO design process significantly (in terms of time and quality) and to achieve a higher quality and functionality of GLOs themselves (in terms of the parameter space enlargement for reuse and adaptation). We demonstrate the appropriateness of the methodology in the real teaching setting. In this paper, we present the case study that analyses three robot-oriented GLOs as the higher-level specifications. Then, using the meta-language processor, we are able to produce, from the specifications, the concrete robot control programs on demand automatically and to demonstrate teaching algorithms visually by robot's actions. We evaluate the approach from technological and pedagogical perspectives using the known structural metrics. Also, we indicate the merits and demerits of the approach. The main contribution and originality of the paper is the seamless integration of two known technologies (feature modelling and meta-programming) in designing robot-oriented GLOs and their supporting tools.